Pablo A. Goloboff

TNT, a free program for phylogenetic analysis

The main features of the phylogeny program TNT are discussed. Windows versions have a menu interface, while Macintosh and Linux versions are command‐driven. The program can analyze data sets with discrete (additive, non‐additive, step‐matrix) as well as continuous characters (evaluated with Farris optimization). Effective analysis of large data sets can be carried out in reasonable times, and a number of methods to help identifying wildcard taxa in the case of ambiguous data sets are implemented. A variety of methods for diagnosing trees and exploring character evolution is available in TNT, and publication‐quality tree‐diagrams can be saved as metafiles. Through the use of a number of native commands and a simple but powerful scripting language, TNT allows the user an enormous flexibility in phylogenetic analyses or simulations.

Analyzing Large Data Sets in Reasonable Times: Solutions for Composite Optima

New methods for parsimony analysis of large data sets are presented. The new methods are sectorial searches, tree‐drifting, and tree‐fusing. For Chase et al.s 500‐taxon data set these methods (on a 266‐MHz Pentium II) find a shortest tree in less than 10 min (i.e., over 15,000 times faster than PAUP and 1000 times faster than PAUP*). Making a complete parsimony analysis requires hitting minimum length several times independently, but not necessarily all “islands” for Chase et al.s data set, this can be done in 4 to 6 h. The new methods also perform well in other cases analyzed (which range from 170 to 854 taxa).

Improvements to resampling measures of group support

Several aspects of current resampling methods to assess group support are reviewed. When the characters have different prior weights or some state transformation costs are different, the frequencies under either bootstrapping or jackknifing can be distorted, producing either under‐ or overestimations of the actual group support. This is avoided by symmetric resampling, where the probability p of increasing the weight of a character equals the probability of decreasing it. Problems with interpreting absolute group frequencies as a measure of the support are discussed; group support does not necessarily vary with the frequency itself, since in some cases groups with positive support may have much lower frequencies than groups with no support at all. Three possible solutions for this problem are suggested. The first is measuring the support as the difference in frequency between the group and its most frequent contradictory group. The second is calculating frequencies for values of p below the threshold under which the frequency ranks the groups in the right order of support (this threshold may vary from data set to data set). The third is estimating the support by using the slope of the frequency as a function of different (low) values of p; when p is low, groups with actual support have negative slopes (closer to 0 when the support is higher), and groups with no support have positive slopes (larger when evidence for and against the group is more abundant).

Weighting against homoplasy improves phylogenetic analysis of morphological data sets

The problem of character weighting in cladistic analysis is revisited. The finding that, in large molecular data sets, removal of third positions (with more homoplasy) decreases the number of well supported groups has been interpreted by some authors as indicating that weighting methods are unjustified. Two arguments against that interpretation are advanced. Characters that collectively determine few well‐supported groups may be highly reliable when taken individually (as shown by specific examples), so that inferring greater reliability for sets of characters that lead to an increase in jackknife frequencies may not always be warranted. But even if changes in jackknife frequencies can be used to infer reliability, we demonstrate that jackknife frequencies in large molecular data sets are actually improved when downweighting characters according to their homoplasy but using properly rescaled functions (instead of the very strong standard functions, or the extreme of inclusion/exclusion); this further weakens the argument that downweighting homoplastic characters is undesirable. Last, we show that downweighting characters according to their homoplasy (using standard homoplasy‐weighting methods) on 70 morphological data sets (with 50–170 taxa), produces clear increases in jackknife frequencies. The results obtained under homoplasy weighting also appear more stable than results under equal weights: adding either taxa or characters, when weighting against homoplasy, produced results more similar to original analyses (i.e., with larger numbers of groups that continue being supported after addition of taxa or characters), with similar or lower error rates (i.e., proportion of groups recovered that subsequently turn out to be incorrect). Therefore, the same argument that had been advanced against homoplasy weighting in the case of large molecular data sets is an argument in favor of such weighting in the case of morphological data sets.

Methods for Quick Consensus Estimation

A method that allows estimating consensus trees without exhaustive searches is described. The method consists of comparing the results of different independent superficial searches. The results of the searches are then summarized through a majority rule, consensed with the strict consensus tree of the best trees found overall. This assumes that to the extent that a group is recovered by most searches, it is more likely to be actually supported by the data. The effect of different parameters on the accuracy and reliability of the results is discussed. Increasing the cutoff frequency decreases the number of spurious groups, although it also decreases the number of correct nodes recovered. Collapsing trees during swapping reduces the number of spurious groups without significantly decreasing the number of correct nodes recovered. A way to collapse branches considering suboptimal trees is described, which can be extended as a measure of relative support for groups; the relative support is based on the Bremer support, but takes into account relative amounts of favorable and contradictory evidence. More exhaustive searches increase the number of correct nodes recovered, but leave unaffected (or increase) the number of spurious groups. Within some limits, the number of replications does not strongly affect the accuracy of the results, so that using relatively small numbers of replications normally suffices to produce a reliable estimation.

Continuous characters analyzed as such

Quantitative and continuous characters have rarely been included in cladistic analyses of morphological data; when included, they have always been discretized, using a variety of ad hoc methods. As continuous characters are typically additive, they can be optimized with well known algorithms, so that with a proper implementation they could be easily analyzed without discretization. The program TNT has recently incorporated algorithms for analysis of continuous characters. One of the problems that has been pointed out with existing methods for discretization is that they can attribute different states to terminals that do not differ significantly—or vice versa. With the implementation in TNT, this problem is diminished (or avoided entirely) by simply assigning to each terminal a range that goes from the mean minus one (or two) SE to the mean plus one (or two) SE; given normal distributions, terminals that do not overlap thus differ significantly (more significantly if using more than 1 SE). Three real data sets (for scorpions, spiders and lizards) comprising both discrete and quantitative characters are analyzed to study the performance of continuous characters. One of the matrices has a reduced number of continuous characters, and thus continuous characters analyzed by themselves produce only poorly resolved trees; the support for many of the groups supported by the discrete characters alone, however, is increased when the continuous characters are added to the analysis. The other two matrices have larger numbers of continuous characters, so that the results of separate analyses for the discrete and the continuous characters can be more meaningfully compared. In both cases, the continuous characters (analyzed alone) result in trees that are relatively similar to the trees produced by the discrete characters alone. These results suggest that continuous characters carry indeed phylogenetic information, and that (if they have been observed) there is no real reason to exclude them from the analysis.

TNT version 1.5, including a full implementation of phylogenetic morphometrics

Version 1.5 of the computer program TNT completely integrates landmark data into phylogenetic analysis. Landmark data consist of coordinates (in two or three dimensions) for the terminal taxa; TNT reconstructs shapes for the internal nodes such that the difference between ancestor and descendant shapes for all tree branches sums up to a minimum; this sum is used as tree score. Landmark data can be analysed alone or in combination with standard characters; all the applicable commands and options in TNT can be used transparently after reading a landmark data set. The program continues implementing all the types of analyses in former versions, including discrete and continuous characters (which can now be read at any scale, and automatically rescaled by TNT). Using algorithms described in this paper, searches for landmark data can be made tens to hundreds of times faster than it was possible before (from T to 3T times faster, where T is the number of taxa), thus making phylogenetic analysis of landmarks feasible even on standard personal computers.

An Optimality Criterion to Determine Areas of Endemism

A formal method was developed to determine areas of endemism. The study region is divided into cells, and the number of species that can be considered as endemic is counted for a given set of cells (= area). Thus, the areas with the maximum number of species considered endemic are preferred. This is the first method for the identification of areas of endemism that implements an optimality criterion directly based on considering the aspects of species distribution that are relevant to endemism. The method is implemented in two computer programs, NDM and VNDM, available from the authors.

Phylogenetic analysis of 73 060 taxa corroborates major eukaryotic groups

Obtaining a well supported schema of phylogenetic relationships among the major groups of living organisms requires considering as much taxonomic diversity as possible, but the computational cost of calculating large phylogenies has so far been a major obstacle. We show here that the parsimony algorithms implemented in TNT can successfully process the largest phylogenetic data set ever analysed, consisting of molecular sequences and morphology for 73 060 eukaryotic taxa. The trees resulting from molecules alone display a high degree of congruence with the major taxonomic groups, with a small proportion of misplaced species; the combined data set retrieves these groups with even higher congruence. This shows that tree‐calculation algorithms effectively retrieve phylogenetic history for very large data sets, and at the same time provides strong corroboration for the major eukaryotic lineages long recognized by taxonomists.

PARSIMONY AND WEIGHTING: A REPLY TO TURNER AND ZANDEE

Turner and Zandee (1995) criticize my (Goloboff, 1993a) method for character weighting. I had proposed that data should always be analyzed taking into account character weights, regardless of the results under equal weights. With that in mind, I proposed a method, derived from Farris’ (1969) ideas on weighting, based on the notion that comparing the fit of different trees using concave, decreasing functions of the homoplasy automatically weights the characters according to the homoplasy they have on the trees being compared. For strongly concave functions, characters with homoplasy have very little influence in tree comparisons, while under less concave functions (i.e. functions approaching linearity) characters with homoplasy are allowed almost as much influence as those without (i.e. trees are compared only according to prior weights). In Pee-Wee, a computer program that implements the method (Goloboff, 1993b), I measured the fit of character i with J=k/(k+es), where k is a constant (equal to or greater than unity) that changes the concavity of the fitting function (to allow homoplastic characters to have less or more influence), and es is the number of extra steps. Pee-Wee searches for trees which maximize the total fit, F=ZJ, with searching algorithms analogous to those of other parsimony programs. As concave decreasing functions of the homoplasy were already used to estimate character weights (Farris, 1969; Farris, 1989), I proposed that trees of maximum fit can also be seen as the trees which imply the characters to be maximally reliable. Turner and Zandee now present some supposedly new findings regarding my method, and they conclude from those that it has some serious faults. I shall here show that the “new” findings are not such-they are well-known facts-and that they constitute in themselves no evidence against my method of weighting, or any other. Most of the present arguments had already been presented in my 1993a paper, or by other authors, but Turner and Zandee have either missed or ignored them. To avoid simply referring the reader to other papers, I shall review the arguments here, and it will then become clear that there is no basis for Turner and Zandee’s criticism.